Processing facilities are typically managed using process control systems. Example processing facilities include manufacturing plants, chemical plants, crude oil refineries, and ore processing plants. Motors, catalytic crackers, valves, and other industrial equipment typically perform actions needed to process materials in the processing facilities. Among other functions, the process control systems often manage the use of the industrial equipment in the processing facilities.
In conventional process control systems, controllers are often used to control the operation of the industrial equipment in the processing facilities. The controllers could, for example, monitor the operation of the industrial equipment, provide control signals to the industrial equipment, and generate alarms when malfunctions are detected.
Advanced controllers often use model-based control techniques to control the operation of the industrial equipment. Model-based control techniques typically involve using models to analyze input data, where the models identify how the industrial equipment should be controlled based on the input data being received. Model-based control techniques have been widely accepted throughout the process control industry. Studies have determined that model-based control techniques can greatly improve the performance of processing facilities and provide significant economic benefits. However, the benefits provided by model-based control techniques depend heavily on the quality of the models being used. As a result, techniques have been developed to validate the models to ensure that the models have an acceptable quality.
Conventional techniques for validating a model typically involve injecting external signals into a process control system or performing open loop testing of the process control system. Both techniques typically disrupt the normal operation of the process control system, which may disturb the normal operation of the entire processing facility. Also, conventional model validation techniques typically either (i) do not analyze historical operating data associated with a controller that uses a model, or (ii) use the historical operating data to produce incorrect or misleading results. This may be due to the fact that only a small fraction of the historical operating data is usually relevant to model validation, and the operating data is often contaminated by noise or other disturbances.